Efficient Intrusion Detection on Low-Performance Industrial IoT Edge Node Devices

Niedermaier, Matthias; Striegel, Martin; Sauer, Felix; Merli, Dominik; Sigl, Georg

doi:10.48550/arxiv.1908.03964

Cited by 2 publications

(1 citation statement)

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“…LSTM neural networks belong to the family of Recurrent Neural Networks (RNNs), a class of ANN architecture which excels in processing data in sequence, such as a sequence sensor readings or a sequence of words in the ield of Natural Language Processing (NLP). Niedermaier et al [51] found that a single IDS running on the network perimeter could not be able to monitor, capture and analyze all the events. They proposed a distributed IDS based on multiple IIoT agents' edge devices and a central unit which uni ies the logs produced by them.…”

Section: The Edge-enabled Approachmentioning

confidence: 99%

Intrusion detection systems for IoT: Opportunities and challenges offered by edge computing

Spadaccino¹,

Cuomo²

2022

ITU J-FET

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Key components of current cybersecurity methods are the Intrusion Detection Systems (IDSs), where different techniques and architectures are applied to detect intrusions. IDSs can be based either on cross-checking monitored events with a database of known intrusion experiences, known as signature-based, or on learning the normal behavior of the system and reporting whether anomalous events occur, named anomaly-based. This work is dedicated to the application of IDS to the Internet of Things (IoT) networks, where also edge computing is used to support IDS implementation. Specific attention is given to IDSs which leverage device classification at the edge. New challenges that arise when deploying an IDS in an edge scenario are identified and remedies are proposed.

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Section: The Edge-enabled Approachmentioning

confidence: 99%

Intrusion detection systems for IoT: Opportunities and challenges offered by edge computing

Spadaccino¹,

Cuomo²

2022

ITU J-FET

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Ensuring cybersecurity for industrial networks: A solution for ARP-based MITM attacks

Bruschi,

Di Pasquale,

Lanzi

et al. 2024

JCS

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The increased adoption of the Internet Protocol (IP) in ICSs has made these systems vulnerable to the same security risks that are present in traditional IT environments. The legacy nature of ICSs and their unique operational requirements make them vulnerable to security threats that are different from those in IT environments. In this paper, we describe a protocol, named ArpON, which is able to wipe out in quasi real time any ARP cache poisoning attempt, thus making it ineffective. Contrarily to solutions presented in the literature for contrasting ARP cache poisoning, ArpON incurs in low operational costs, is backward compatible, transparent to the ARP protocol and does not use any HW feature nor cryptography functionality. We also model and validate ArpON in the OMNET + + network simulator. The simulation results show that ArpON is effective in avoiding ARP poisoning, and its communication overhead is negligible with respect to classical ARP protocol.

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Efficient Intrusion Detection on Low-Performance Industrial IoT Edge Node Devices

Cited by 2 publications

References 0 publications

Intrusion detection systems for IoT: Opportunities and challenges offered by edge computing

Intrusion detection systems for IoT: Opportunities and challenges offered by edge computing

Ensuring cybersecurity for industrial networks: A solution for ARP-based MITM attacks

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